Following previous work Wang et al., 2018), we adopt the same split settings in our experiments, where 30% of the ILLs are used as training and the remaining 70% for evaluation.

the coverage of ILLs among existing KGs is quite low (Chen et al., 2018): for example, less than 20% of the entities in DBpedia are covered by ILLs.


This suggests that our model can capture the characteristics of the source dataset via pretraining when using small supervision from language adaptation (i.e., small α).

the evaluation results on public benchmark datasets and comparison against current state-of-the-art approaches demonstrate the effectiveness of our approach.


For instance, Bowman (2013) poses generalization tasks in which entire reasoning patterns are held out for testing.

to assign these labels, we translate each premisehypothesis pair into first-order logic and use Prover9 (McCune, 2005(McCune, -2010.


We now formalize the idea of recursive tree-structured composition and this intuitive notion of fairness.

this enables us to create provably fair NLI tasks in Section 4.


It would not be possible to use pretrained word vectors, due to the artificial nature of our dataset.

the essence of natural logic reasoning is recursive composition up a tree structure where the premise and hypothesis are composed jointly, so this bottleneck proves extremely problematic.


We add the new task of also predicting why the actions are needed, in the form of the actions' effects (blue) and subsequent actions that depend on those effects (green).

this analysis suggests several ways that the dependency graph computation could be improved.


The 2014 ProRead system (Scaria et al., 2013;Berant et al., 2014) included dependency relationships between events that it extracted, but assessed dependencies based on surface language cues, hence could not explain why those dependencies held.

they are unaware if they predict effects that have no apparent purpose in the process, possibly indicating a prediction error (e.g., the erroneous predictions in red in Figure 1).


We found that time-constrained AMT annotators performed well (i.e., > 70%) accuracy for k ≤ 3 but struggled with examples involving longer stories, achieving 40-50% accuracy for k > 3.

overall, we find that the GAT baseline outperforms the unstructured text-based models across most testing scenarios (Table 2), which showcases the benefit of a structured feature space for robust reasoning.


To generate these stories, we first design a knowledge base (KB) with rules specifying how kinship relations resolve, and we use the following steps to create semi-synthetic stories based on this knowledge base: Step 1.

unlike previous benchmarks in this domain-which are generally transductive and focus on leveraging and extracting knowledge graphs as a source of background knowledge about a fixed set of entities-CLUTRR requires inductive logical reasoning, where every example requires reasoning over a new set of previously unseen entities.


Enabling an automated system to hold a coherent task-based conversation with a human remains one of computer science's most complex and intriguing unsolved problems (Weizenbaum, 1966).

at the same time, the machine-oriented context of the interaction, i.e.


Beyond the corpus and the methodologies used to create it, we present several baseline models including state-of-the-art neural seq2seq architectures together with perplexity and BLEU scores.

learning from purely human-human based corpora presents challenges of its own.


We combine these ELMO and GloVe embeddings via concatenation.

name, SSn number) or they can be unique to a domain-specific intent (e.g.


As a sanity check, we also include a most frequent class (MFC) baseline.

past dialogue datasets do not make this distinction at speech act schema level.


(2018); throughout the rest of the paper, we use the BERTbased architecture in our experiments.

by round 3, however, workers struggle to trick the system, earning an average score of only 1.6 out of 5.


We can find that the GECOR model beats the baseline model in all respects.

for these cases, we tested our models and made statistical analysis on the three versions of data as shown in column 3, 4 and 5 of Table 3 (EM, EM 1, EM 2).


Both probabilities from the two modes contribute to the final probability distribution over the extended vocabulary (the vocabulary plus the words from the dialogue context) which is calculated as follows: which is used to predict the final output word.

we only integrate the GECOR model with the copy mechanism into the dialogue system.


the closest gas station is located at 200 alester ave 7 miles away would you like directions there Mem2Seq there is a valero 1 miles away HMNs there is a gas station located 2 miles away at 200 alester ave We test the hidden size in [64,128,256] and set dropout rate in [0.1, 0.2].

attention weights in the last hop of the two memories,P kb and P his will be the probability of the target word from those memories.


The gating mechanism applied is adopted from Bidirectional GRU (Cho et al., 2014a) in our case.


Then the representation of each node is updated with graph convolution operation with normalization factor (Kipf and Welling, 2017) as below: where g l−1 j ∈ R 2d h is the j-th token's representation evolved from the preceding GCN layer while h l i ∈ R 2d h is the product of current GCN layer, and A ij is degree of the i-th token in the tree.

(2014) containing twitter posts, while the other four (LAP14, REST14, REST15, REST16) are respectively from SemEval 2014 task 4 (Pontiki et al., 2014), SemEval 2015 task 12 (Pontiki et al., 2015) and SemEval 2016 task 5 (Pontiki et al., 2016), consisting of data from two categories, i.e.


This is because the DMI can infer relational representations that capture some common latent relations between aspect and opinion words.

for the second stage, the domain classification loss is minimized by the domain discriminator parameters θ d while maximized by the feature learning parameters θ f via GRL (i.e., the features are   (Pontiki et al., 2014).


To solve that, the full model AD-SAL performs a local semantic alignment to dynamically focus on aligning aspect words that contribute more to the domain-invariant feature space.

at the final hop, we adopt a domain discriminator for each word with a gradient reversal layer (Ganin et al., 2016) to perform domain adversarial learning over its correlation vector (alignment).


In such settings, a sentence S with K aspects will be copied to form K instances.

w a 1 ∈ R d×d , w a 2 ∈ R d×d and z a ∈ R d are the weight matrices.


Multi-task learning can learn better hidden states of the sentence, and better aspect embeddings.

(2016) propose an attention-based LSTM network for aspect level sentiment classification.


For each domain, we train our model on the training set without using any aspect labels, and only use the seed words G via the teacher.

although averages of seed words were used as "anchors" in the "Tandem anchoring" algorithm, we observed that the learned topics did not correspond to our aspects of interest.


Thus, using the distillation loss for training, the student learns to use both seed words and non-seed words to predict aspects.

the aspect embeddings A k are initialized by clustering the vocabulary embeddings using kmeans with K clusters.


Aspect labels (9class for product reviews and 12-class for restaurant reviews) are available for each segment 6 of the validation and test sets.

in ABAE, the K topics learned to reconstruct the segments are not necessarily aligned with the K aspects of interest.


Stance of tweets is annotated as support, deny, query, and comment.

the embeddings of a sentence are represented as l×(dp+dw) .


Garcia et al., 1997;Cofield et al., 2010;Lazarus et al., 2015;Chiu et al., 2017).

(2017) adopted a simplified taxonomy from (Sumner et al., 2014) for causal relations in research findings, and used SVM to develop a fourcategory classifier with a 0.718 F1-score, suggesting room for further performance improvement.


While MSA represents the standardized and literary variety of Arabic, there are several Arabic dialects spoken in North Africa and the Middle East in use on Twitter.

mTSL Except for the directness, mTSL usually outperforms STSL or is comparable to it.


This may make the process of optimizing the objective function more difficult and hinder the model to learn representative embeddings for the nodes.

for the vertex classification task, a logistic regression model is first trained to classify the embeddings into different categories based on the provided labels of nodes.


First, we analyze the document counts for each decade bin, shown in Figure 8.

datasets used by political scientists are mostly homogeneous in terms of subject (e.g., immigration) or document type (e.g., constitutions).


Typically, researchers first represent locations as earth grids (Wing and Baldridge, 2011;Roller et al., 2012), regions (Miyazaki et al., 2018;Qian et al., 2017), or cities (Han et al., 2013).

acc@161: The percentage of predicted cities which are within a 161 km (100 miles) radius of true locations to capture near-misses.


Awry is the model previously proposed by Zhang et al.

our predictor consists of a multilayer perceptron (MLP) with 3 fully-connected layers, leaky ReLU activations between layers, and sigmoid activation for output.


To address the growing problem of online hate, an extensive body of work has focused on developing automatic hate speech detection models and datasets (Warner and Hirschberg, 2012;Waseem and Hovy, 2016;Davidson et al., 2017;Schmidt and Wiegand, 2017;ElSherief et al., 2018a,b;Qian et al., 2018a,b).

for binary hate speech detection, we experimented the following four different methods.


Variational Auto-Encoder (VAE) (Kingma and Welling, 2013): The structure of the VAE model is similar to that of the Seq2Seq model, except that it has two independent linear layers followed by the encoder to calculate the mean and variance of the distribution of the latent variable separately.

the reward of the agent is: where rw(c, r) is the reward with regard to the conversation c and its reference response r in the dataset.


Recently, the Pew Research Center (Center, 2017) reported that "roughly four-in-ten Americans have personally experienced online harassment, and 63% consider it a major problem."

as is to be expected with machine-generated text, in the other human evaluation we conducted, where Mechanical Turk workers were also presented with sampled human-written responses alongside the machine generated responses, the human-written responses were chosen as the most effective and diverse option a majority of the time (70% or more) for both datasets.


Metrics Evaluating How Predictions are Made: We need to quantitatively evaluate the difference between how models and humans classify tweets.

for each model, we also report the percentage of tweets that has rationale 0 or 1, respectively.


As λ increases from 0.0 to 1.0, the performance of identifying f alse and unverif ied rumors generally gains.

to show the effect of our customized graph convolution operation (Eq.


The contributions of this work are as follows.

when the supervision signal of stance classification becomes strong, the learned stance features can produce more accurate clues for predicting rumor veracity.


When labeled data is insufficient, existing methods may not work well as expected.

• APJFNN (Qin et al., 2018): It learns a word-level semantic representation for both job requirements and resumes based on RNN and four hierarchical ability-aware attention strategies.


Following previous studies (Vaswani et al., 2017), we set the hidden dimension of our model HGAT and other neural models to d = 512 and the dimension of pre-trained word embeddings to 100.

to address the problem, efforts have been made to enrich the semantics of short texts.


In many practical scenarios, the labeled data is scarce, while human labeling is time-consuming and may require expert knowledge (Aggarwal and Zhai, 2012).

in this way, the sparsity of the short texts is alleviated.


CNN: CNN (Kim, 2014) with 2 variants: 1) CNN-rand, whose word embeddings are randomly initialized, and 2) CNN-pretrain, whose word embeddings are pre-trained with Wikipedia Corpus.

these methods are not able to achieve good performance as the feature engineering step relies on domain knowledge.


We use financial news (2009.10.19 to 2016.10.31) from Sohu 4 to predict the daily movement of Shanghai securities composite index.

with the argument that a piece of news often describes one or several events which could be represented as a collection of elements, this study extracts news elements as enti- ties, actions and nouns from news titles and texts, with respect to their tf-idf scores.


A possible explanation is that the Paragraph Vector model assigns a distinct vector to each of the news articles, thus inconsistency between news in different news browsing sequences is easily created by the sequence-level fine-tuning.

for unsupervised learning, the autoencoder model is an early solution to compress text data (Vincent et al., 2010).


Table 1 shows that the SEQ model outperforms the current SOTA system on all three text genres for binary CWI (statistically significant using McNemar's test, p=0.0016, χ 2 =9.95).

in order to rank substitutes, the simplicity and contextual semantic equivalence scores are combined to produce an overall suitability score.


Previous work has framed this task as ranking words according to simplicity (Paetzold and Specia, 2016a).

threshold-based filtering is performed by removing all substitutes that are unlikely to be grammatical or do not fit the target context.


The gold standard substitution suggested by the human annotators in this context is "demarcated", whereas REC-LS substitutes this word with "split".

these embeddings are able to model complex syntactic and Oak is strong and also gives shade g.c.


1, the second news is more informative than the first news in modeling user preferences, since the first one is usually not audience sensitive.

denote the click probability score of the positive news asŷ + , and scores of the K negative news .


This is because our approaches incorporate both word-and newslevel attention networks to simultaneously select important words and news for learning more informative news and user representations.

first, the characteristics of search queries, webpage titles and news have huge differences.


1, search queries are usually phrase pieces with a few words, while news and webpage titles are usually complete sentences.

first, we visualize the distributions of the view-level attention weights in our NRHUB approach, and the results are shown in fig.


Different from these methods, our approach can learn unified user representations by incorporating heterogeneous user behaviors via an attentive multi-view learning framework.

evaluating the informativeness of different views via a view-level attention network can learn better user representations.


This is probably because reviews usually contain rich clues on user preferences and item properties, which is useful for user and item representation learning for recommendation.

to solve this problem, in our approach we propose a hierarchical attentive graph neural network for recommendation which can model high-order user-item interactions.


We conducted experiments on four widely used benchmark datasets in different domains and  scales to validate the effectiveness of our approach.

it may be effective to recommend Item-2 to User-2.


We use SenticNet (Cambria et al., 2018) to normalize these emotion words (W = {w 1 , w 2 , .

two events with similar word embeddings may have similar embeddings despite that they are quite unrelated, for example, as shown in Figure 1 (b), "PersonX broke record" and "PersonY broke vase".


According to (Hirschauer, 2010), coverage and divergence should be considered for the acceptance decision of a paper.

the deep and wide components jointly make the prediction.


The EoG model outperforms all baselines for all pair types.

they allow mention-mention edges only if these mentions belong to the same entity and consider that a single mention pair exists in a sentence.


We identify three frequent cases of errors, as shown in Table 6.

for the inter-sentence pairs, performance significantly drops with a fully connected graph (Full) or without inference (NoInf).


Through ablation analysis, we observe that the memory buffer and the KG features contribute significantly to this performance gain.

by forming semantic abstractions for each highlighted text span, we decompose a single complex tagging task in a large label space into correlated but simpler sub-tasks, which are likely to generalize better when the training data is limited.


Then we introduce scalable implementations of the Rectified Anchor Word (RAW) algorithm and various evaluation metrics, investigating the impact of each inference step from vocabulary curation to topic inference.

then the main learning task of topic models is to find the wordtopic matrix B and topic-document matrix W that approximates H ≈ BW with the column-stochastic constraints B ∈ CS N ×K , W ∈ CS K×M .


They consist of political blogs (Eisenstein et al., 2011) and business reviews (Lee and Mimno, 2014) with the smallest vocabulary (4.4k, 1.6k), respectively.

most topics involve Pop and Rock, emulating the overall genre distribution of the corpus as illustrated in Figure 5.


While using the pivoted QR (Arora et al., 2013) 6 Avoiding negative entries is useful because RAW is not a purely algebraic algorithm but uses probabilistic conditions.

instead of directly decomposing H, JSMF decomposes smaller but aggregated statistics for revealing the latent topics and their correlations.


Spectral methods explicitly construct word co-occurrence moments as statistically unbiased estimators, providing alternatives to the probabilistic algorithms via moment-matching.

figure 2 shows that running only 5 iterations of AP or DR sufficiently rectifies C, and 15-20 iterations yields almost identical results to 150 iterations.


Given the binary nature of the classification problem we can use the cross entropy loss: where Φ is the set of all trainable weights in the model, N is the number of data samples, m i is the number of sentences in the i-th data sample, p ij is the predicted label of the j-th sentence in the i-th data sample, and y ij is the corresponding ground truth label.

ally, in an ablation study we demonstrate that our various modeling choices, which tackle the inherent challenges of comment-edit understanding, each contribute positively to empirical results.


Such a system would necessarily first need to learn mappings between edits and natural lan-guage comments, before it could learn to generate them.

it becomes difficult to know which tasks have been completed and which haven't, especially if authors are not proactive about marking comments as addressed.


It is important to recall that the main goal of our work is to develop fast and efficient on-device neural text classification approach, which can achieve near state-of-the-art performance while satisfying the on-device small size and memory resource constrains.

baseline Comparison: We use the same baselines as described in Tang et al., 2015).


By following (Kudo, 2018), we can find the most likely segmentation sequence t starting from all of the n-best segmentations t 1 , • • • ,t n of S(p) rather than from onlyt 1 .

this metric remedies shortcomings of common QAC evaluation metrics, mean reciprocal rank (MRR) and partial-matching MRR (PMRR), which require sampling of a prefix length and are favorable to short queries.


Whereas computations run in parallel in GPU, the number of operations for the output layer in the language model is proportional to the vocabulary size in CPU.

to deal with issues coming from introducing subword language model, we develop a retrace algorithm and a reranking method by approximate marginalization.


Query suggestion (Sordoni et al., 2015;Dehghani et al., 2017) and query reformulation (Nogueira and Cho, 2017) are related to QAC and well-established problems.

on the assumption that log p(t) log p(t ) for all t ∈ T B and t / ∈ T B , Equation (1) implies that marginalization over the final beam outputs can provide better approximation: reranking after summing out the probability of duplicates can give better ordered list of candidates.


these limitations arouse to consider alternatives to represent a query in a shorter sequence.


Although current retrace algorithm is implemented straightforwardly, it can be improved by merging beams efficiently.

sR models only obtain small improvement.


To address the challenges of identifying rare and complex disease names, (Xu et al., 2019) proposed a method that incorporates both disease dictionary matching and a document-level attention mechanism into BiLSTM-CRF for disease NER.

in Figure 3, the sentences as S C s and S C t contain the word "cough", andh sm andh tn are the corresponding hidden states.


The results show that our proposed model with global attention significantly outperforms the Bi-LSTM CRFinference model for symptom inference across all the categories.

the results show that our proposed model with global attention significantly outperforms the Bi-LStM CRFinference model for symptom inference across all the categories.


In Figure 3, the sentences as S C s and S C t contain the word "cough", andh sm andh tn are the corresponding hidden states.


Interestingly, however, fine-tuning with the larger set of counterfactuals (CF loss) does not seem to help in rewriting endings that relate to the counterfactuals well.

rob ended up playing the best song of his life.


Only the BERTScore metrics appear to positively correlate with human scores for counterfactual understanding, making them usable for evaluating generations across properties related to all three questions.

model Size and Pretraining Data We observe that models with more parameters are better at the counterfactual rewriting task than smaller models.


crowdworkers were presented outputs of a pair of systems, and asked to choose which one is better, or "equally good" or "equally bad", in terms of each of the three criteria.


The n-grams in the target text that are not part of the LCS are the phrases that would need to be included in the phrase vocabulary to be able to construct t from s.  In practice, the phrase vocabulary is expected to consist of phrases that are frequently added to the target.

3.2 using the validation set of 46K examples.


Here we describe its main components: (1) the tagging operations, (2) how to convert plain-text training targets into a tagging format, as well as (3) the realization step to convert tags into the final output text.

for a batch size 8, LASERTAGGER AR is already 10x faster than comparable-in-accuracy SEQ2SEQ BERT baseline.


With the two networks, we can factorize the generation probability P (C|T, B) as P (S|T, B) • P (C|S, T ), where S = (s 1 , .

in iR-T and iR-TC, we use three types of filters with window sizes 1, 3, and 5 in the CNN based matching model.


The number of samples in Monte Carlo sampling is 1.

the attention mechanism selects useful information in the title, and the gate mechanism further controls how much such information flows into the representation of the article.


Supposing we have a smaller vocabulary, the document represented by this vocabulary is y = {y i } m i=1 , where m < n is the number of words and y i ∈ R e is the word embedding of the word at position i.

but it is not good at modeling semantics (Dieng et al., 2017) and may cause the latent variable collapse problem (bowman et al., 2016) which leads that the decoder ignores information from the inferred latent codes.


propose to adopt a Gaussian-based topic model which assumes each word is generated by a Gaussian distribution.

the i-th word generated by the decoder is where W v ∈ R |V |×e denotes word embedding.


Without bounding box regression, the Soft-Label Chain CRF model has an accuracy of 69.85%, a 4.84% reduction compared to the setting with bounding box regression.

overlapping regions can be penalized only after prediction, so this loss is not differentiable, and one has to resort to reinforcement learning.


For each of the targeting pronoun, the workers are asked to select all the mentions that it refers to.

we align mentions in the dialogue with objects in the image and then jointly use the contextual and visual information for the final prediction.


Then, we rank all segment predictions by the scoreŝ: A straightforward solution is selecting the segment with the highest score as the final prediction.

2) The positive and negative training samples are extremely imbalanced.


we set the ground truth confidence of each frame based on its "centerness" in the segment.


Table 1 presents some typical examples of our corrections.


Note that, our proposed framework is general and fits most of, if not all, NER models that accept weighted training data.

we follow the standard train/dev/test splits and use both the train set and dev set for training (Peters et al., 2017;Akbik et al., 2018).


", this "Chicago", representing the NBA team Chicago Bulls, should be annotated as an organization.

another usage of CrossWeigh is to identify potential label mistakes during label annotation process, thus improving the annotation quality.


More importantly, we propose a simple yet effective framework, CrossWeigh, to handle label mistakes during NER model training.


high recall), while keeping away from wrongly identified non-mistake sentences (i.e.

no matter "Chicago" is LOC or ORG, it only counts as its surface name "Chicago".


When k becomes larger, each fold D i will be smaller, thus leading to a smaller size of test_entities i ; correspondingly, a larger train_set i will be picked.

when annotators are not careful or lack background knowledge, this "Chicago" might be annotated as a location, thus being a label mistake.


The Test Performance compares the performance of the NER models trained with these annotations.

for brevity of space, we compare results for two languages in figure 3: 3 Dutch, a relative of English, and Hindi, a distant language.


When we count the number of entities present in the data selected by the two strategies, we see in Figure  4(b) that data selected by ETAL has a significantly larger number of entities than SAL, across all the 6 annotation experiments.

the reason for this thresholding is purely for computational purposes as it allows us to discard all spans that have a very low probability of being an entity, keeping the number of spans actually stored in memory low.


The details of the NER model hyper-parameters can be found in the Appendix.

the performance of these models is highly dependent on the availability of large amounts of annotated data, and as a result their accuracy is significantly lower on languages that have fewer resources than English.


Moreover, this strategy minimizes annotator effort by requiring them to label fewer tokens than the full-sequence annotation.

for SAL we see that the annotator missed a likely entity because they focused on the other more salient entities in the sequence.


The improvements are robust and significant on both tasks, both metrics, and on all depths.

the transformer is convolutional on all length k of n-grams; the same parameters are used model the interactions between n-grams at each length, to reduce the parameter size.


Further, we set the ijk element of the tensor W ∈ R ne×nr×ne to 1 if the fact (e s , r, e o ) holds and -1 otherwise.

typically, P , Q, R are smaller than I, J, K respectively, so Z can be thought of as a compressed version of X .


As described in the methodology section, the label remapping function f L we use, is a one-to-one mapping between the labels of the original task and the adversarial task.

white-box based reprogramming outperforms the black-box based approach in all of our experiments.


i) Reuters21578: A dataset consisting of 10788 news documents from 90 different categories; ii) 20N ewsgroups: A collection of 18828 newsgroup posts that are divided into 20 different newsgroups; iii) T M C: A dataset containing the air traffic reports provided by NASA, which includes 21519 training documents with 22 labels.

despite of superior performances, only the simplest priors are used in these models, i.e.


Given a document x, its hashing code can be obtained through two steps: 1) mapping x to its latent representation by z = µ φ (x), where the µ φ (x) is the encoder mean µ φ (•); 2) thresholding z into binary form.

it is shown that the endto-end training brings a remarkable performance improvement over the two-stage training method in VDSH.


We can see that the retrieval precisions of the proposed models, especially the BMSH, are quite robust to this parameter.

it is widely known that priors play an important role on the performance of generative models (Goyal et al., 2017;Chen et al., 2016;Jiang et al., 2016).


The greedy search algorithm is also used in the graph construction for candidates collecting.

later a theoretical solution for MIPS by searching on the Delaunay Graphs will be summarized.


By Definition 2.2, a data point is isolated (i.e., have no incident edges) if its Voronoi cell is empty.

at beginning of the graph construction, relatively "super" points are not true extreme points.


For embedding based methods (word2vec, GloVe, D-embedding 7 , and MWE 8 ), we follow previous work (Mikolov et al., 2013;Levy and Goldberg, 2014) and use the cosine similarity between embeddings of head and tail words to predict their relations.

• GloVe (Pennington et al., 2014), learning word embeddings by matrix decomposition on word co-occurrences.


Several studies attempted to acquire SP automatically from raw corpora (Resnik, 1997;Rooth et al., 1999;Erk et al., 2010;Santus et al., 2017).

assuming that there are 20 relations among words, the size of all embedding models with the increasing word numbers in Figure 4. word2Vec and GloVe have the smallest size because they only train one embedding for each word while sacrificing the relation-dependent information among them.


end end end end drifting range a, whose value controls the distance between the local relational embeddings and the center embedding.

in this paper, we propose a multiplex word embedding (MWE) model, which can be easily extended to various relations between two words.


Deep neural language models with a large number of parameters have achieved great successes in facilitating a wide range of Natural Language Processing (NLP) applications.

we call each group a codebook, and each d m dimensional vector in the codebook a codeword.


In particular, quantization (Lin et al., 2016;Hubara et al., 2016) and groupwise low rank approximation (Chen et al., 2018a) achieve the state-of-the-art performance on tasks like language modeling and machine translation.

we refer to our proposed method as MulCode (Mul stands for both multi-way and multiplicative composition).


pose quantitative methods for evaluating bias in word embeddings of gendered languages and bilingual word embeddings that align a gendered language with English.

for example, if "doctor" (male doctor) leans more toward to masculine (i.e., close to masculine definition words like "el" (he) and far away  figure 2: Projections of occupation words on the semantic gender direction in two gender forms (blue for male and red for female) in Spanish, the original English word (grey), and the bias-mitigated version of the English word (black with "*").


of this bias, the embeddings may cause undesired consequences in the resulting models (Zhao et al., 2018a;Font and Costa-jussà, 2019).


Some failure cases in the 25 examples occur for pairs where the set of translations of y e contains an incorrect translation which is totally unrelated to x e or y e .

recent work on semantic relation prediction largely focuses on a single relation between words in the same language (mostly English) (Nastase et al., 2013;Vulić and Mrkšić, 2018;Glavaš and Ponzetto, 2017;Ono et al., 2015).


While the contextualized representations in the BERT hidden layer vectors are features that determine the word sense, some features are more useful than the others.


End-to-end neural machine translation (NMT) (Sutskever et al., 2014;Bahdanau et al., 2015) learns to generate an output sequence given an input sequence, using an encoder-decoder model.

on average across the test sets, their approach did not outperform SVM with word embedding features.


We attempt to apply DynSP * (trained on SQA) directly on FollowUp test set, which results in an extremely low AnsAcc.

the main idea is to decompose the task into two phases by introducing a learnable intermediate structure span: two queries first get split into several spans, and then undergo the recombination process.


For example, givenz as "where are the players from",w could be "Las Vegas".

structured Query Language) and retrieves the answer from databases regardless of its context.


To this end, we design a two-phase process and present a novel approach STAR to perform follow-up query analysis with reinforcement learning.

we propose to initialize SplitNet via pre-training, and then use reward to optimize it.


Given x, y, during training of Phase I (in blue), we fix P rec to provide the reward R(q, z), then P split can be learnt by the REINFORCE algorithm.

let f j = softmax(A :,j ), where f j ∈ R n denotes the attention weights on x according to y j .


Besides, we use 3 layers BiLSTM with 400-dimensional hidden states, applying dropout with an 80% keep probability between time-steps and layers.

our baseline is a modification to the model of  which uniformly handled the predicate disambiguation.


According to our statistics of syntactic rule on training data for seven languages, which is based on the automatically predicted parse provided by CoNLL-2009 shared task, the total number of distance tuples in syntactic rule is no more than 120   in these languages except that Japanese is about 260.

based on gold syntax, the top-1 argument pruning for Catalan and Spanish has reached 100 percent coverage (namely, for Catalan and Spanish, all arguments are the children of predicates in gold dependency syntax tree), and hence our syntax-aware model obtains significant gains of 1.43% and 1.35%, respectively.


BERT is also a transformer encoder that has access to the entire input but this choice requires a special training regime.

examples in BooksCorpus are a mix of individual sentences and paragraphs; examples are on average 36 tokens.


Finally, we include the BERT results from Yang et al.

increasing the number of Bi-LSTM layers can sometimes even hurt, as shown in Figure 2b.


Inspired by the correlation, researchers try to improve SRL performance by exploring various ways to integrate syntactic knowledge (Roth and Lapata, 2016;He et al., 2018b;.

our experimental result boosts to 88.5 F1 score when the framework is enhanced with BERT representations.


First, a set of phrase alignments A is obtained for s, t Figure 2: Our method injects semantic relations to sentence representations through paraphrase discrimination.

it is also obvious that the three-way classification of phrasal paraphrases, on which the model discriminates paraphrases, random combinations of phrases from a random pair of sentences, and random combinations of phrases in a paraphrasal sentence pair, is superior to binary classification.


Results indicate that sentential and phrasal paraphrase classification complementarily contributes to Simple Transfer Fine-Tuning modeling.

our method learns to discriminate phrasal and sentential paraphrases on top of the representations generated by BERT.


(2018), we canonicalize strings in the target cell and truncate them to a maximum of 120 code tokens.

automatically making these decisions conditioned on prior history is one of the main challenges of this task.


Table 2 presents dataset statistics for JuICe.

we tackle the task of general purpose code generation in an interactive setting, using an entire sequence of prior NL and code blocks as context.


Although there has been work on describing a SQL query with an NL statement (Koutrika et al., 2010;Ngonga Ngomo et al., 2013;Iyer et al., 2016;Xu et al., 2018), few work studies generating questions about a certain SQL component in a systematic way.

the transition can be deterministic or stochastic.


Intuitively if the base semantic parser gives a low probability to the top prediction at a step, it is likely uncertain about the prediction.

for SQLNet, we also compare our system with the reported performance of DialSQL (Gur et al., 2018, Table 4).


Similarly, (Yao et al., 2019) relies on a pre-defined two-level hierarchy among components in an If-Then program and cannot generalize to formal languages with a deeper structure.

a more direct comparison of various settings under the same average number of questions can be found in appendix C. To better understand how each kind of error detectors works, we investigate the portion of questions that each detector spends on right predictions (denoted as "Q r ").


When the agent interacts with users, the maximum number of alternative options (in addition to the original prediction) per component, K, is set to 3.

for SQLNet, MISP-SQL outperforms the DialSQL system with only half the number of questions (1.104 vs. 2.4), and has a much simpler design without the need of training an extra model (besides training the base parser, which DialSQL needs to do as well).


1 In order to distinguish the edge direction, we add a direction symbol to each label with ↑ for climbing up along the path, and ↓ for going down.

unsurprisingly, this will end up with a large number of features.


We calculate F 1 scores based on the partial match instead of exact match.

• A p is used to denote that the current word is part of a sentiment span with polarity p, but appears after the target word or exactly as the last word of the target.


Existing research efforts mostly regard this joint task as a sequence labeling problem, building models that can capture explicit structures in the output space.

we consider the following baselines: • Pipeline (Zhang et al., 2015) and Collapse (Zhang et al., 2015) both are linearchain CRF models using discrete features and embeddings.


They modelled the latent sentiment scope based on CRF with latent variables, and achieved the best performance among all the existing works.

the difference between EI and SA-CRF is that our model EI captures flexible explicit structures in the output space  table 3: Results on subjectivity as well as non-neutral sentiment analysis on the Spanish dataset.


This procedure aims to adjust the contribution of each capsule based on the high-level capsule (i.e.

high-level capsules) to maximize the sum of the weighted log probabilities that the Gaussian would generate the datapoints (i.e.


Our ultimate training goal is to minimize the loss function with parameters θ = {θ f , θ y , θ g , θ } as follow: Whereλ 1 , λ 2 and λ 3 are the weight parameters to balance the importance of losses between the emotion detection and the two personal attribute discriminators.

moreover, the adversarial discriminators are much more effective than the attention mechanisms.


This information gap at both ends of the decoder causes the decoder needs to bear both the target sentiment addition and content reconstruction.

decoder: Given the hidden representations , the decoder generates words sequentially.


It not only has a variety of practical applications, e.g., fighting offensive language on social media (dos Santos et al., 2018), but also serves as an ideal testbed for controllable text generation.

in addition, we train the sentiment classifier (Kim, 2014) to filter samples with the category confidence below 0.8.


In the second step, the decoder does not include the sentiment information in its source input, meaning that the source information is incomplete for generating a sentimenttransferred sentence.

the training of RL is unstable and sensitive to hyper-parameters.


Indeed whereas such resources have been gathered in the case of textual data and can be used to deeply understand the expression of opinions (Wiebe et al., 2005;Pontiki et al., 2016), the different attempts at annotating multimodal reviews have shown that reaching good annotator agreement is nearly impossible at a fine grained level.

this strategy and the following one are introduced to test whether the order in which the tasks are introduced has some importance on the final scores.


(2018a): we represent each sentence by taking the average of the feature representation of the Tokens composing it.

the price to pay to gather reliable data is the definition of an annotation scheme focusing on coarse grained information such as long segment categorization as done by Zadeh et al.


Furthermore, the text used in these datasets often uses the same set of words to express clearly contrastive sentiments, thereby setting up groups or domains of distinct word use within a single data set.

accordingly, the KCCa domain adapted word embeddings are indeed capturing something significant about the language usage in the domain.


The domain adapted word w i,DA is then passed as input to the CNN encoder as shown in the figure.

a model that generates interpretable, structured sentence embeddings using Self-attention mechanism (Lin et al., 2017).


These auxiliary tasks focus on directly enhancing domain-invariant features, while ours strips domain-specific features to distill domaininvariant features.

this indicates that DIFD can learn purer domain-invariant features than ASC+At.


The statistics of these three datasets are shown in Table 1.

next, we will introduce the components of our method in detail.


Table 2 shows the performance comparison of different approaches.

given a review document with a clause sequence and an aspect, the high-level policy decides whether a clause mentions this aspect.


In this paper, to simulating the steps of analyzing aspect sentiment in a document by human beings, we propose a new Hierarchical Reinforcement Learning (HRL) approach to DASC.

a well-behaved approach should discard noisy clauses for a specific aspect during model training.


Such a response makes several assumptions which are clearly violated here, such as the involvement of government.

finally, asking again about a claim's mention enables result validation, when the answer is known a-priori with high confidence.


2018a), are orthogonal to our proposed attribute representation and injection method.

the first task is Sentiment Classification, where we are tasked to classify the sentiment of a review text, given additionally the user and product information as attributes.


We expect this paper to serve as the first investigatory paper that contradicts to the positive results previous work have seen from the bias-attention method.

it has been well perceived, that using only the user and product attributes to generate text is unreasonable, since we expect the model to generate coherent texts using only two vectors.


One crucial emotion cause cue is sentiment words since emotion causes usually express a certain sentiment polarity by sentiment words.


Furthermore, for the English dataset, our proposed model has balance performance in precision and recall.

rHNN manages to boost the performance by 3.06% in F-measure compared to Lamb-daMArT, which exhibits that by restraining the parameters with knowledge-based regularizations, rHNN is better to identify the emotion cause cues than feature engineering.


The arguments collected had to have 8 -36 words, aimed at obtaining efficiently phrased arguments (longer/shorter arguments were rejected by the UI).

assessing argument quality has driven practitioners in a plethora of fields for centuries from philosophers (aristotle et al., 1991), through academic debaters, to argumentation scholars (Walton et al., 2008).


To overcome these limitations, we propose a novel task: performing fine-grained analysis of texts by detecting all fragments that contain propaganda techniques as well as their type.

(2) and (3) into an F 1 -measure, the harmonic mean of precision and recall.


Is it right that they should pay for their son's murderer to be fed and housed?"

we designed an annotation schema of 18 propaganda techniques, and we annotated a sizable dataset of documents with instances of these techniques in use.


In this paper, we introduce a recurrent neural network based approach for the multi-modal sentiment and emotion analysis.

we argue that the encoded representation learns the interaction between the text and acoustic modalities.


For each dataset, we report three best systems for the comparisons 2 .

this performance degradation suggests that the IIM module is, indeed, an important component of our proposed architecture.


Similar work on feature-level fusion based on self-attention mechanism is reported in (Hazarika et al., 2018).

we employ another IIM to capture the interaction between the visual-text.


Our observations in this experiment are the following: • CNNs fail to fully incorporate sequential information as performance on random ordering and correct ordering are marginally near each other.

we employ two different architectures of SCARN model since the datasets vary in size.


These approaches provided strong baselines for text classification (Wang and Manning, 2012).

here we make a distinction between the semantic knowledge of a word and the task specific knowledge of a word.


We observe that SCARN significantly outperforms concat-SCARN on all the datasets.

this does not relate the same way of selecting most relevant features from convoluted features in texts.


This leads to another question, if the feature selected by max pooling will always be the most important feature of the input or otherwise.

a new feature vector C i is obtained for each word w i after convolving with K filters.


After computing the attention scores, the final context representation v d is computed as follows: We then concatenate the context representation with the target claim representation and pass it to the classification layer to predict the quality.

claims with higher impact parents are more likely to be have higher impact.


One recent study in NLP has investigated the role of argument sequencing in argument persuasion looking at (Hidey and McKeown, 2018) Change My View 1 , which is a social media platform where users post their views, and challenge other users to present arguments in an attempt to change their them.

this structure enables us to study the characteristics of impactful claims, accounting for the effect of the pragmatic context.


Incorporating the parent representation only along with the claim representation does not give significant improvement over representing the claim only.

we find that the BERT model with claim only representation performs significantly better (p < 0.001) than the baseline models.


Based on this assumption we aggregate only the aspect embeddings using a max pool with the aim to retain most of the information.

most recent methods have focused on leveraging neural networks (Chen et al., 2017;Gu et al., 2018;majumder et al., 2018;Fan et al., 2018a;Xue and Li, 2018;Huang and Carley, 2018;Zheng and Xia, 2018) which model representations automatically.


We conduct an experiment to demonstrate that the performance of our proposed models, namely CDT and ASP-GCN, depend on the number of layers of the GCN.

tNet on the other hand measures the association between 'Sangria' and 'good' in both directions to identify the correct 'good'.


We perform a subsequent run of ASP-GCN on the same sentence s to extract a final embedding, but in this instance we conceal a specific input word w. We conceal w by mapping it to the zero vector before applying ASP-GCN on s. As a result a final embedding h (s\w) is generated for s. If h s = h (s\w) , the word w has no impact on the representation h s .

61772059,  61421003), by the Beijing Advanced Innovation Center for Big Data and Brain Computing (BDBC), by State Key Laboratory of Software Development Environment (No.


Even with simple architectures, we find that ASP-BiLSTM and ASP-GCN have competitive performance with the recent models on benchmark datasets.

together with node embeddings modeled by BiLStMs, we can exploit a GCN capable of operating directly on graphs.


According to our empirical analysis, the percentage of repetitive words drops from 8.3% to 6.5% by our proposed methods on the IWSLT14 De-En test set, which is a 20%+ reduction.

during training, the predictions at different positions can be estimated in parallel since the ground truth pair (x, y) is exposed to the model.


As we explain in §3, our investigation requires a definition of lexical semantics that is independent of grammatical gender.

for example, almost all Spanish nouns ending in -a are feminine, whereas Spanish nouns ending in -o are usually masculine.


The MIL approach for document classification is illustrated in Figure 1.

for 159 cities that have representation of the three crimes both in Patch and FBI crime reports, we calculate the ratio of Patch-based predictions to FBI reports for each crime.


We also appreciate anonymous reviewers for constructive review comments.

(2016)) only use the given sentences, which suffer from the low efficiency problem in capturing long-range dependency; dependency tree based methods utilize the syntactic relations (i.e., arcs) in the dependency tree of a given sentence to more effectively capture the interrelation between each candidate trigger word and its related entities or other triggers.


In this paper, we propose a Hierarchical Modular Event Argument Extraction (HMEAE) model, to provide effective inductive bias from the concept hierarchy of event argument roles.

taking Figure 1 as an example, "Seller" is conceptually closer to "Buyer" than "time-within", because they share the same superordinate concepts "Person" and "Org" in the concept hierarchy.


3The argument role classifier relies on the instance embedding and the role-oriented embedding to estimate the probability of a certain argument role for the instance.

we design a neural module network for each basic unit of the concept hierarchy, and then hierarchically compose relevant unit modules with logical operations into a role-oriented modular network to classify a specific argument role.


The whole procedure is trained end-to-end, with the model learning simultaneously how to identify important links between spans and how to share information between those spans.

for each variation, we study the effect of integrating different task-specific message propagation approaches.


Following the experimental settings and evaluation metrics in Bai and Zhao (2018), we use two most-used splitting methods of PDTB data, denoted as PDTB-Lin (Lin et al., 2009), which uses sections 2-21, 22, 23 as training, validation and test sets, and PDTB-Ji (Ji and Eisenstein, 2015), which uses 2-20, 0-1, 21-22 as training, validation and test sets and report the overall accuracy score.

these models typically use pre-trained semantic embeddings generated from language modeling tasks, like Word2Vec (Mikolov et al., 2013), GloVe (Pennington et al., 2014) and ELMo (Peters et al., 2018).


(2018) fed external world knowledge (ConceptNet relations and coreferences) explicitly into MAGE-GRU (Dhingra et al., 2017) and achieved improvements compared to only using the relational arguments.

it is a corpus annotated over 24 open access fulltext articles from the GENiA corpus (Kim et al., 2003) in the biomedical domain.


(2017) (i.e., short binary patterns, why-QA system's answers, and sentences with clue terms).

(2013)'s CRF-based causality recognizer and used them as a causality-rich corpus while assuming that the cause and effect are sentence pairs for the next-sentence prediction task (9,783,691 pairs or 19,567,382 sentences).


as the parameter settings of the pre-training for all of the BERT models, we followed the BERT BaSE settings 6 (Devlin et al., 2019) except for a batch size of 50.


While this yielded a model that was quite well-matched to the EEG modeling task, its training data was confined to just 1543 sentences (24K words).

the importance of domain adaptation in NLP has been well-established in earlier work (see Daumé III, 2007 and footnote 1), including applications to parsing (Sarkar, 2001;McClosky et al., 2006;Søgaard and Rishøj, 2010;Weiss et al., 2015).


In contrast, the development of distributional modelling techniques and the availability of vast text corpora have allowed researchers to construct effective vector space models of word meaning over large lexicons.

in this context, G and F represent GloVe embedding vectors and property norm feature vectors, respectively.


For Feature2Vec, we Kingfisher has wings does fly has a beak has feathers is a bird does eat has a tail does swim has legs does lay eggs Avocado is eaten edible is tasty does grow is green is healthy is used in cooking has skin peel is red is food is a vegetable Door made of metal has a door doors is useful has a handle handles made of wood made of plastic is heavy is furniture does contain hold is found in kitchens Dragon is big large is an animal has a tail does eat is dangerous has legs has claws is grey is small does fly Table 3: Top 10 predictions from CSLB-trained Feature2Vec model.

this comes at the cost of interpretable, human-like information about word meaning.


To this end, our ConVQA Dataset consists of two subsets: 1) a challenging human-annotated set comprised of commonsense-based consistent QA's (shown in  Figure 3), and 2) an automatically generated logicbased consistent QA dataset (shown in Figure 2).

man", an appropriate entailed question might be "Is the tennis court empty?".


Our Consistency Checker has a high accuracy of classification on the L-ConVQA test set (90%).

we compare to a number of baseline models to put our CTM results into context:  Table 1 shows quantitative results on our L-ConVQA and CS-ConVQA datasets.


we tested several state-of-the-art methods for question answering, textual entailment, and reading comprehension on our GeoSQA dataset.


The 22 diagram categories and 81 text templates are not predefined but incrementally induced during the experiment.

we would like to thank all the annotators.


Each category is associated with a set of text templates that are recommended to be used in annotations as far as possible.

finally, we choose the option with the highest score as the answer.


(2017), where the Watson-Paths system is presented to answer questions that describe a medical scenario about a patient and ask for diagnosis or treatment.

compared with GeoSQA, their datasets are small and, more importantly, they ignore diagrams which represent a unique challenge to geographical SQA.


Algorithm 1 achieves perfect accuracy on ToM-easy and ToM.

first-Order Belief B: Where will B look for O?


For this reason, we propose to systematically ask all question types for each generated story.

towards Robust toM Evaluation in QA to increase robustness against such regularities and correlations, we build upon the ideas of toM-bAbi but improve data generation and evaluation in multiple ways.


(3) Quasar-T (Dhingra et al., 2017) and 4SearchQA (Dunn et al., 2017) are leveraged with the official split.

then the vector representation of each word position from BERt encoder is fed into two separate dense layers to predict the probabilities P s and P e (Devlin et al., 2018).


The answer A should be a span which is directly extracted from the passage P. According to most of the works on SQuAD, the task can be simplified by predicting the start and end pointer in the passage (Wang and Jiang, 2016).

• No more than five questions for each passage.


Each Γ n (W) ∈ R |W |×d represents n-gram semantics.

unigram CNN uses 1:1 word matching, and is able to utilize word-based signals as query term weighting value.


This variance is especially high for local IDF, which serves as a strong signal as consistently observed in (Blair-Goldensohn et al., 2003).

contextualizing into word-level representation makes it natural to combine with word-level IDF scores in the model, and also enables indexing (Hwang and chang, 2005).


Non-factoid questions, unlike factoid questions answered by short facts like a word or a phrase, may get answered by a long answer spanning across multiple sentences.

due to a typical length difference between a question and a long answer in our problem setting, most answer words are left unmatched, except a few uni-gram in the answer.


Edge vectors are similarly added when summing over node representations to produce the new output representations.

creating labeled data for this task can be expensive and time-consuming.


Quadruplet loss extends triplet loss to ensure smaller intra-class distances and larger interclass distances.

all our models ignore any interaction between question and answer representations, and use a shared encoder to independently encode questions and answers.


However, although QuaRel contains 2700 qualitative questions, its underlying qualitative knowledge was specified formally, using a small, fixed ontology of 19 properties.

they were then asked to author a situated, 2-way multiple-choice (MC) question that tested this relationship, guided by multiple illustrations.


The control vector c is a randomly initialized learnable vector.

at each hop, a single control vector is used to interpret each memory cell independently.


Combined with a simple transfer learning technique from a large-scale online corpus, our model achieves new state-of-the-art results on the TrecQA and WikiQA datasets.

our proposed techniques do add a significant amount of performance.


Previous paraphrase generation work on QUORA (Gupta et al., 2018; Li et al., 2018) did not mention removing these entries, thus we include them in our experiments for fair comparison.

both full parroting and the forms of partial parroting we use are fully unsupervised.


Full parroting simply uses the input as output (o = i).

for future paraphrase generation research which achieve good evaluation scores, we suggest investigating whether their methods or models act differently from simple parroting behavior.


Annotators assigned scores on a fivepoint scale, with 1 and 5 indicating that the summary is bad or good with respect to a specific Q.

differences among systems are thus small in this respect, and although SUM-QE predicts scores in this range, it struggles to put them in the correct order, as illustrated in Figure 3.


Further, at generation time, they heavily rely on rejection sampling to produce quatrains which satisfy any valid rhyming pattern.

we show that the discriminator induces an accurate rhyming metric and the generator learns natural rhyming patterns without being provided with phonetic information.


We concatenate the word embedding e t , answer position embedding a t and lexical features embedding l t as input ( Then a bidirectional LSTM is used to produce a sequence of hidden states (h 1 , ..., h T ).

assuming m+1, ..., m+a is the index of given answer span in the input sentence, based on the corresponding featurerich hidden states (h m+1 , ..., h m+a ), we calculate the question type hidden states as follow: where l m+j is the corresponding lexical features.


To obtain a combined training objective of two tasks, we add the loss of question type prediction into the loss of question generation to form a total loss function: 3 Experiment Dataset Following the previous works, we conduct experiments on two datasets, SQuAD and MARCO.

we reduce the question type vocabulary to only question words.


Many innovative deep RL methods (Ranzato et al., 2016;Wu et al., 2016;Paulus et al., 2018;Lamb et al., 2016) are developed to alleviate this issue by providing sentence-level feedback after generating a complete sentence, in addition to optimal transport usage (Napoles et al., 2012).

(2018) do, because we want to examine the repetition of sentence directly produced after training.


Sequence Matching task has attracted lots of attention in the past decades.

then we extend BERt with a MatchLStM layer to get further interaction of the sentence pair for sequence matching tasks.


Our first approach relies on the usage of textbooks from specific domains of short answer grading.

these include our own textbooks and additional ones downloaded from Lumen Learning † and Gutenberg ‡ websites.


Universal Language Model Fine-Tuning (ULMFiT) (Howard and Ruder, 2018) method is one of the first such initiatives to illustrate the effectiveness of language model fine-tuning.

our findings of the sensitivity of domain-specific BERT models appear generic.


Compared with CB B , MNLI+CB B improves the overall performance of contradiction and the recall of neutral.

cB B and MNLI+cB B performances on the Yes-items are similar and both outperforming MNLI B (statistically significant improvements, McNemar's test, p < 0.01).


To avoid such lists we used the following rules: (i) for English, sentences have to start with an uppercase letter and end with a period; (ii) for Nepali and Sinhala, sentences should not contain symbols such as bullet points, repeated dashes, repeated periods or ASCII characters.

we first use automatic methods to filter out poor translations and send those translations back for rework.


Improving translation performance on low-resource language pairs could be very impactful considering that these languages are spoken by a large fraction of the world population.

we observed large variations in the level of translation quality, which motivated us to enact a series of automatic and manual checks to filter out poor translations.


This work introduces conditional masked language models and a novel mask-predict decoding algorithm that leverages their parallelism to generate text in a constant number of decoding iterations.

mask-predict repeatedly masks out and repredicts the subset of words in the current translation that the model is least confident about, in contrast to recent parallel decoding translation approaches that repeatedly predict the entire sequence .


In traditional left-to-right machine translation, where the target sequence is predicted token by token, it is natural to determine the length of the sequence dynamically by simply predicting a special EOS (end of sentence) token.

we find that our approach significantly outperforms prior parallel decoding machine translation methods and even approaches the performance of standard autoregressive models (Section 4.2), while decoding significantly faster (Section 4.3).


Comparing with the co-occurrence relation, which subjects to contexts and topics of a document, word association test bears a more direct interaction with human brains.

we snapshot a sub-graph of G with P * in Figure  1 to show how stereotype propagates.


If the score exceeds 3 the word "love" is considered as the seed positive sentiment phrase to begin the bootstrapping procedure a threshold (i.e., p > 0.5), the candidate phrase is added to the gazetteer.

moreover, it ends up generating only 20 different responses for our entire test dataset making its output redundant and unrelated to the input.


Reference: you have to sacrifice your first born child for swimming lessons in nyc.

for training this module, the sarcasm corpus S is used.


The learning algorithm minimizes negative log likelihood for optimizing parameters.

we also incorporate explicit means for reducing redundancy during decoding.


Unlike the current implementation of SYPHON, these models include representational structures that enable them to capture certain non-local phenomena.

for illustration purposes, in all examples we will assume that our feature set has just three features: voice v, sonorant s, and continuant c. The input to this step is the matrix of surface forms X ij and the output is a set of aligned pairs U,X ij .


Inference must be data-efficient: typically only a handful of data points are available.

this is a very computationally intensive problem.


For example, in the following sentence, "In 2005 two fed- eral agencies, the US Geological Survey and the Fish and Wildlife Service, began to identify fish in the Potomac and tributaries ...", the model should use "federal agencies" to help classify "US Geological Survey" and "Fish and Wildlife Service" as government agency, but focus on "fish" and "tributaries" to determine that "Potomac" should be a body of water (river) instead of a city.

we also present a hybrid classification method beyond binary relevance to exploit type interdependency with latent type representation.


There have indeed been such attempts, e.g., in clinical narratives Tourille et al., 2017) and in newswire (Cheng and Miyao, 2017;Meng and Rumshisky, 2018;Leeuwenberg and Moens, 2018).

we should move along that direction to collect more high-quality data, which can facilitate more advanced learning algorithms in the future.


Note that the KB type representation is obtained from a knowledge base through entity linking and is independent of the context of the mention.

these datasets are more preferred by recent studies (Ren et al., 2016a;Murty et al., 2018).


the next baseline is a hypergraph-based method by Muis and Lu (2016a).


Our model learns representations of these lower-arity relations using the universal schema framework, and predicts new n-ary facts by aggregating scores of lower-arity facts.

for the one derived from a textual relation, we use the following encoders to compute its representations.


For example, in Figure 1 all subsequences of the sentence, such as "George Washington", will first be encoded, * Xianpei Han is the corresponding author.

to better decouple name and context knowledge for incorporating gazetteer information, we first design a new region-based attentive neural network (ANN), which introduces attention mechanism (Bahdanau et al., 2014;Vaswani et al., 2017;Su et al., 2018) to explicitly model the association between mentions and contexts.


To create a corpus of candidate phrases and manually confirmed VAs, we use the dataset of Fischer and Jäschke (2019) as starting point and their method as baseline.

the popularity measure could easily be changed to any other measure like statements or using Wikipedia clickstream data.


In this paper, we presented approaches for the first fully automated extraction of the stylistic device Vossian Antonomasia.

instead of relying only on one individual syntactic pattern, the EN-TITY of, we extended the candidate generation process with the patterns the ENTITY among, the EN-TITY for, and six variations thereof where the is pattern  replaced by a or an, respectively.


We can see that HanPaNE+P showed the highest accuracy and HanPaNE+P and VE+P, with consideration of paraphrases, showed a higher accuracy than Baseline.

hence, chemical NER has been studied to recognize chemical compounds from chemical * Taiki Watanabe belonged to Ehime University when this work was done.


(wc) and LS T M (sc) are shared in NER and paraphrase generation.

hanPaNE-P and VE-P, without consideration of paraphrases, did not.


This work was supported by Contracts HR0011-15-C-0113 and HR0011-18-2-0052 with the US Defense Advanced Research Projects Agency (DARPA).

this isolates the importance of dealing with casing, and makes our observations applicable to situations where modifying the model is not feasible, but retraining is possible.


For sentences involving reporting verbs like said, told, asked, etc., some systems annotate additional attributional context for every utterance (Mausam et al., 2012).

this can be attributed to the fact that this dataset was not manually curated for Open IE, rather QA-SRL data was adapted for this task.


In original OIE2016 paper, the authors "match an automated extraction with a gold proposition if both agree on the grammatical head of all of their elements (predicate and arguments)".

it does not reward partial coverage of gold tuples, and forces one system prediction to match just one gold.


We see that the model primarily loses out on the text based measure, and performs quite well on the type based one.

this issue can potentially be mitigated with either a more sophisticated tagger module or a better stopword/symbol removal mechanism, which we leave to future work.


This results in the model picking out spurious words alongside the actual entity, which necessitates the use of stop-word and symbol removal.

we observe that our proposed approach significantly outperforms the baseline, and performs reasonably well when compared to various stateof-the-art supervised approaches that use significantly more ground-truth annotation information.


We experiment both with using the embeddings as is, and fine-tuning the top layers of the attention encoder (we only try fine-tuning the top, toptwo and top-three layers due to computational constraints).

this usually requires a large amount of fine-grained data annotated at the token level, which in turn can be expensive and cumbersome to obtain.


The motivation is that the EUs cannot cross over a sentence.


It would be very difficult to infer the correct stance label if we do not consider the hashtags "#NoHillary" and "#VoteGOP."


First, an ablation experiment is used to determine the importance of each component of our proposed model for stance detection: • AT-JSS-Lex is a lexicon integrated multi-task model with attention mechanisms.

we construct a stance lexicon for each target from the training data available from the Se-mEval 2016 dataset and from an extra 1,000 tweets for each target that we collected from Twitter using specific hashtags.


We compare our method against three different baselines.

they delete sentiment-specific phrases (e.g., "works great") from a given sen-(a) the service was great too → the service was n't too great (b) this one is by far the worst → this one is the best by far Figure 1: the retrieved sentences (right) from the opposite corpora given the query sentences (left).


Given a corpora with vocabulary size V , we represent each token x i ∈ {0, 1} V as a one-hot vector with a single nonzero entry at v. Here, v ∈ V represents the word type at position i in the text.

substantive interpretability is key for CssDH research, and thus, the lack thereof is a major limitation.


We examine the proposed priors using three commonly sized English corpora for textual analysis within CSSDH: the top 100 list of books in Project Gutenberg (2019), a sample from Twitter (Go et al., 2009) and the U.S. Congress Speeches 1981-2016 (Gentzkow et al., 2018).

consequently, the dot product for these vectors will be 0 for all dimensions except K, and thus the effect of the anchored word types on the rest of the vocabulary will exclusively depend on K. this implies that, as γ, ω → 0, word types within V − and V + obtain exactly the same word embedding.


In socio-linguistic literature even though there are many studies that observe propagation of hate speech (Ribeiro et al., 2018;Salminen et al., 2018) and abusive behaviour (Founta et al., 2018;Maity et al., 2018;Mathew et al., 2019a,b) in social media an in-depth analysis of how sarcastic message travels in social networks and how tweets around the targets behave is an area which social scientists need to investigate.

• Multiple sarcasm targets: There can be multiple sarcasm target phrases present in the sentence.


Sarcasm target identification can also benefit natural language generation; for example, after detection of entity toward which a negative sentiment is expressed in a sarcastic text, a natural language generation system will have more context to generate a response.

if a user expresses a sarcastic utterance such as "My laptop has an awesome battery life that lasts for 15 minutes", the tool should recognize that the speaker is expressing a negative sentiment toward the battery life of the laptop, even though, it has a positive sentiment word 'awesome' in it.


Results show that identifying informational bias poses additional difficulty and suggest future directions of encoding contextual knowledge from the full articles as well as reporting by other media.

in order to evaluate token-level prediction on the larger original test set, we conduct a pipeline experiment with the fine-tuned BERT models where sentences predicted as containing bias by the best sentence-level classifier from cross validation are tagged by the best token-level model.


A visual inspection indicates that this may be attributed in part to media sources' attempts to hook readers with inflammatory speech early on (e.g., FOX: "Paul Ryan stood his ground against a barrage of Biden grins, guffaws, snickers and interruptions.").

the results reaffirm our hypothesis that while both tasks are extremely difficult, informational bias is more challenging to detect.


From YouTube, we collect broadcast football transcripts and identify mentions of players, which we link to metadata Authors contributed equally.

we now demonstrate confounds in the data and revisit several established results from racial bias studies in sports broadcasting.


Recently, there has been a resurgent interest in this task due to the availability of more data and new machine learning techniques (Luo et al., 2017;Zhong et al., 2018b;.

we stack multiple blocks of an LSTM layer and a charge-specific gating layer for generating a focused charge-based representation of the case description.


First, the humor judgments differ from individual to individual.

for each prompt, the writing staff of the show pick a top 10 and an overall winner.


In this way, we can use only a small portion of labeled data to predict the remaining unlabeled data effectively (Zhou et al., 2003).

w s (i, j) indicates the frequency that word w i and w j cooccur in s within the window H. In this way, we can capture the lexical patterns of s in w s .


Different words in the same news may have different importance in representing this news.

besides, we apply additive attentions to both news and user encoders to select important words and news to learn more informative news and user representations.


The proposed method benefits from multiple transferable knowledge and shows competitive performances with the state of the art using limited resources.

we replace English words with target language words if the translation pairs exist in the bilingual dictionaries (Rolston and Kirchhoff, 2016).


Unfortunately, the proposed method fails to find the famous anagram for these inputs.

since the number of FSA states grows exponentially with input length, it rapidly becomes intractable.


Anagram Artist can generate anagrams consisting of at most 5 words given inputs with lengths less than 25 characters.

since the superiority of neural language models over n-gram models has been reported in previous works (e.g., (Du et al., 2016;Józefowicz et al., 2016)), we conducted experiments using only ELMo.


The current state-of-the-art model is based on reinforced learning using the (in)equivalence of two regular expressions.

stockmeyer and Meyer (1973) showed that it is PsPACE-complete to decide if two regular expressions generate the same set of words.


The embedding dimension size is set to 4, since the size of vocabulary for regular expressions is relatively small compared to natural languages.

there are situations that even expert humans cannot accurately classify.


We used Adam Optimizer to train all our models.

we dropped all clinical notes which doesn't have any chart time associated and also dropped all the patients without any notes.


They are then asked to guess the L1 translation of each L2 word type that appeared at least once in the passage.

our teacher does not yet attempt to monitor the human student's actual learning.


For instance, the Automatic Content Extraction (ACE) (Doddington et al., 2004) corpus contains more than 500 documents manually annotated with examples for 33 event types.

there is a long tail of examples where long range dependencies need to be resolved in order to type them correctly.


Second, we adopt bidirectional LSTMs (J) for CATD-MATCH, without an obvious improvement, probably because most messages in the datasets can be fully comprehended only with previous history.

searching the entire space of T is unfeasible.


If the sentiment classifying knowledge about how to comment on these concepts can be acquired, it will be helpful for sentiment classification when meeting these concepts in free texts again.

♦ Sentiment classification is a hard task, and it needs subtly describing capability of language model.


Classical technology in text categorization pays much attention to determining whether a text is related to a given topic [1], such as sports and finance.

all these work enlightened us on the research on Concerned Concepts in given domain.


'Headlines and NE' denotes the best result obtained by our method, i.e.

we explore two linguistically motivated restrictions on the set of words used for tracking: named entities and headline words.


A topic is related to a specific place and time, and an event refers to notions of who(person), where(place), when(time) including what, why and how in a story.

4) and (2) the tracking using various number of word sequences (Fig.


Overall, the result of 'with hierarchy' was better than that of 'without hierarchy' in all N t values.

dragon Systems proposed two tracking systems; one is based on standard language modeling technique, i.e.


In work with non-parallel corpora, contexts of source language terms and target language terms and a seed translation lexicon are combined to measure the association between the source language terms and potential translation candidates in the target language.

after retrieving some sub-documents for a given topic from the target corpus, we take a set of top ranked sub-documents, regarding them as relevant sub-documents to the query, and extract terms from these sub-documents.


In the recent past, many models for automatic image annotation are limited by the scope of the representation.

this model combines both asymmetric clustering model which maps words and image regions into clusters and symmetric clustering model which models the joint distribution of words and regions.


In order to generate appropriate annotations, a simple language model is developed that takes the word-correlation information into account, and then the textual description is determined not only by the model linking keywords and blob-tokens but also by the word-to-word correlation.

due to rough fixed-size rectangular grids, the extracted blocks are unable to model objects effectively, leading to poor annotation accuracy in our experiment.


In this paper, the cooccurrence between nouns and verbs is measured by mutual information.

japanese relative clause modification should be classified into at least two major semantic types: case-slot gapping and head restrictive.


For nouns that do not tend to be modified by 'outer' clauses, such as " "(people), " " (city), and " "(television), the ratio between the frequency and the number of verbs is almost the same between the relative clause and case-slot cases.

murata [11] presented a statistical method of classifying whether the relative clause is an 'inner' or an 'outer' clause.


Given the types of Japanese relative clause constructions and a corpus of Japanese relative clause construction instances, we present a machine learning based approach to classifying RCC's.

in this paper we present eight features are effective in classifying case-slot gapping and head restrictive relative clauses.


The results we obtained from evaluation revealed that our method outperformed the traditional case frame-based method, and the features that we presented were effective in identifying RCC's types.

they judged the instances as 'outer' clauses, only if case-slot filling did not succeed.


The percentage of " " cooccurring with noun 7.

the accuracy of this feature is not so good compared with other features.


Our interpretation of this latter result is that the primary limitation of the lexicon is coverage, despite its size.

this simple fix raised tagging accuracy to 95.9%.


These dependencies are neither too fine-grained nor too coarsegrained compared with bilexical and biclass dependencies, and really help to alleviate fundamental information sparseness in treebank.

and that may be also true for discriminative models, since these models can easily incorporate richer features in a wellfounded fashion.


Nominal Compounds (NCs) Disambiguation: Nominal compounds are notorious "every way ambiguous" constructions.

the coordinated structure VP is parsed as: V P 0 → V P 1 IP 2 .


As this method requires only a search engine, it can segment texts that are normally difficult to process by using language tools, such as institution names (5,6), colloquial expressions (7 to 10), and even some expressions taken from Buddhist scripture (11,12).

we can also consider the reverse order, which involves calculating h for the previous element of x n .


For example, it is easier to guess what comes after x 6 ="natura" than what comes after x 5 = "natur".

this assumption is illustrated in Figure 1.


The reason for the choice of languages lies in the fact that the process utilised here is based on the key assumption regarding the semantic aspects of language data.

this paper verifies assumption (A) in a fundamental manner.


We assumed that attributes that conformed to the naturalness criterion would be such important attributes.

we refer to this set of documents as a local document set (LD(C)).


However, we think this attribute is also useful in practice for people who have a keen interest in foreign films.

figure 5 has graphs for the general attribute case the same as for the relaxed case.


Furthermore, it is much easier to choose and access information for solving problems by using the answers of our QA system than by using the answers of the full text retrieval system.

our knowledge base is composed of these labeled sets of a question and its answer mail.


However, these kinds of documents requires the considerable cost of developing and maintenance.

questioner's reply mails are questioner's answers to the direct answer mails.


Table 4 (c) shows the number and type of confirmation labels which were given to proper answers.

the precision of the significant sentence extraction was emphasized in this task.


When multiple rules match an input string at a given position, the longest-matching rule is selected.

6, only the lexical entries for the second and the fourth morphemes in the substring are selected as additional lexical information, and none of the contexts is selected in this case.


Experiments on the STEP 2000 dataset showed that the proposed method yields an F-score of 95.36%.

firstly, we explore characteristics and chunk types of Korean.


Since Ramshaw and Marcus approached NP chunking using a machine learning method, many researchers have used various machine learning techniques [2,4,5,6,10,11,13,14].

if we use more features such as semantic information or collocation, we can obtain a better performance.


The real parsing performances of accepting input from automatic word segmentation and pos tagging system are shown in the Table 5.

the mother-node feature is the least effective feature, since syntactic structures do not vary too much for each phrase type while playing different grammatical functions in Chinese.


The binarization method proposed in our system is different from CNF.

a better approach is to generalizing and specializing rules under linguistically-motivated way.


Only the applicable patterns for each word were increased.

grammar generalization and specialization methods were discussed in section 3.


In our experiments, we consider grammars suited for PCFG parsing.

they also suffer problems of over-generation and structure-ambiguity.


it looks simple at first glance since there are a very small number of punctuations, namely, period (".


This result suggests that our method could capture proper places of MCT pairs with this level of precision.

the criterion for judgment conformed to that of ordinary dictionaries, i.e., the evaluator judges whether given a word pair would be described as a synonym by an ordinary dictionary.


Since our method does not cut off acquired synonyms by frequency, synonyms that appear only once can be captured.

their method considers sentential word ordering.


The problem is to search the most probable class assignment for each entity and each relation of interest, given the observations of all entities and relations.

for example, if "Boston" is mislabeled as a person, it will never have chance to be classified as the location of Poe's birthplace.


The ITG level of expressiveness constitutes a surprisingly broad equivalence class within the expressiveness hierarchy of transduction grammars.

‚oe $ Ì' º « $ j , 1 -( Ï<p (If two classifier suspected person bei-particle sentence guilty, then must in Scotland serve time .)


This paper reports a new way of extracting terms that is tuned for a very small corpus.

a corpus of each size was generated by singly adding an article randomly selected from the corpus of each genre.


Note that the proportion of negative samples and positive samples is 1:6.

in the second step, each document identified unreliable in the first step are further processed by exploring the dependences among features.


Note that the data used in the preliminary experiments described in Section 3.3 are a part of Corpus A.

examples of sp-scores include five-star and scores out of 100.


The last word of a baseVP represents the entire baseVP to which it belongs.

whereas he deals only with subjects and objects as verb-noun co-occurrence, we used all the kinds of co-occurrence mentioned in Sect.


In Korean, a head word usually appears after its modifying words.

1 shows a part of decision tree learned by C4.5 algorithm.


First, instead of only classifying relations by assuming all candidate relations are detected, we perform relation detection before classifying relations by regarding every pair of entities in a sentence as a target of classification.

aCE corpora consist of 519 annotated text documents assembled from a variety of sources selected from broadcast news programs, newspapers, and newswire reports [1].


As we will discuss in section 3 about features, we are working on very high dimensional feature space, and this often leads to overfitting.

for example, relations such as "a town west of Jerusalem", "park outside Paris", "his friend/wife/brother" are highly dependent on the lexical feature, i.e., "west of", "outside", and "friend/wife/brother" are the most important clue to determine the class of the relations.


This means that we are not restricted to the limited types of relations defined in MUC [1] or ACE [22].

the numbers inside parentheses in table 1 and table 2 correspond to the statistical values of the NE pair "PER-GPE", while the numbers outside parentheses are related to the NE pair "COM-COM".


This approach does not rely on any annotated corpus and works effectively on high-frequent entity pairs [8].

the replacement of summation with maximization reduces the computational time greatly.


As indicated in subsection 2.1, the "Head Word" of root node defines the main meaning of a parse tree.

there are two problems in this approach: • The assumption that the same entity pairs in different sentences have the same relation.


In this paper, we focus on the last learning paradigm, i.e., transductive learning.

then we combine the multiple binary classifiers and get a single classifier In the example above, a label "PARt" is eventually assigned to the tuple.


If we compare the performance presented in this subsection with those of the corresponding transductive learners in the previous subsection, we observe the following pattern: NB < SGT < SNoW-SGT < SNoW < SVM-SGT < SVM With regard to the purpose of this study, again, it is most important to notice that the induction-aided transductive learners significantly outperform the "pure" transductive learner.

with the SVM-SGT learner, we get a 78.04% accuracy, and the class-specific performance is summarized in Table 3.


Parse the sentences using the Charniak parser [14].

when it comes to exploiting unlabeled data, the tradeoff between the last two is not yet well understood.


-IOB-chains of the heads of the two entities, each of which is a lexicalized path, in other words, a concatenation of the syntactic categories of all the constituents on the path from the root node to this leaf node of the tree (e.g., "S/VP/NP/NN").

it may be worth the effort to investigate other alternatives.


Word unigrams, word bigrams, and semantic categories of nouns are used as features.

the decrease of F-measure suggests the effectiveness of the excluded feature set.


Since whether a noun is a product name or not is important for PROD-UCT as discussed before, semantic categories of nouns are crucial to PRODUCT.

score(s i ) indicates the likeliness of s i being the core sentence.


Although there is a frequently-used expression "What is [noun] -?

important clues for WAY are phrases such as "How do I".


Only the features from the target sentence would not be enough for accurate classification.

in this work, we treat only questions which require one answer.


Languages are unique in syllable structures.

to this, though the letter " " exists in Sinhala writing system (corresponding to the consonant sound /j/), it is not considered a phoneme in Sinhala.


Hereafter, we will use a source grapheme for a source language grapheme and a target grapheme for a target language grapheme.

many previous works make use of either source grapheme or phoneme.


This is because unweighted voting in this paper requires more than half of the word aligners in the ensembles to vote for the same link.

this procedure is the same as the one to construct training sets for N-fold cross-validation.


Figure 1 shows some examples of morphological information by Chinese, Japanese, English and Korean morphological analyzers and Figure 2 the correspondences among the words.

under the framework of log-linear models, we investigate the effects of morphosyntactic information with word representation.


As the syntactic role of each word within Japanese and Korean sentences are often marked, word order in a sentence plays a relatively small role in characterizing the syntactic function of each word than in English or Chinese sentences.

in this paper, we address the question of the effectiveness of morpho-syntactic features such as parts-of-speech, base forms, and relative positions in a chunk or an agglutinated word for improving the quality of statistical machine translations.


Then in the test step, we perform morphological anlysis of a given sentence for word representation corresponding to training corpus representation.

we could get some advantageous Korean morpho-syntactic information in the Chinese-to-Korean translation, i.e., the advantage of language and translation models using morpho-syntactic information.


Then we grab the phrase translation pairs that contain at least one intersected word alignment and some unioned word alignments [1].

korean and Japanese sentences have a relatively free word order; whereas words within Chinese and English sentences adhere to a rigid order.


This limits the part-of-speech of the words to be exchanged to nouns and verbs.

w k wizard utterances directly following the k highest ranking utterances Step 2: for each w i ∈ {w 1 , .


If we have sufficient data for a specific question pattern like "how long," we will have more chances to obtain alignment counterparts that are effective terms for query expansion.

consider the example of gathering answer passages from the Web for the (Q, A) pair where Q = "What is the capital of Pakistan?"


Perform part-of-speech tagging on Q and AP.

we want to find out what terms are associated with "how old" in the answer passages.


Taking into account of the four position features, the final character tag set is comprised of 184 tags.

in hidden Markov model, given the sentence,  The best character tagging result for the sentences is given by equation 2: We used the People's Daily Corpus of 1998 [13] to train this model.


Our approach is better than the word-based method for two test data sets.

we tried to incorporate a lexicon to the model to improve the performance.


Among all the features used, only a few might be very relevant to recognizing the non-compositionality of the MWE.

some preliminary work on recognition of V-N collocations was presented in [28].


[12] and IBM phrase-based Model 4 used in IBM method D are very similar in modeling.

x permutations of the all phrases in 1 x e % .


To reduce vocabulary size and avoid sparseness, we constrain the phrase length to up to three words and the lower-frequency phrase pairs are pruned out for accurate phrase-alignment 1 .

although PM functions as an ngram language model, it only models the ordering connectivity between target language phrases, i.e., it is not in charge of target word selection.


In MDS, the source documents could contain multiple topics.

she breaks each sentence into features and takes the vector product of features as follows: Feature conditional probabilities can be calculated using frequency counts of features as follows: Lapata [3] uses nouns,verbs and dependency structures as features.


For example, the first four PCs can justify more than 90% (91.28%) of the total sample variance, we can choose them without much loss information.

the U and V are the vectors for the 9 sentences and 9 terms respectively.


In this subsection, we will outline PCA which is adapted from [6] and which is used to extract the significant terms in the document.

to word frequency, the other method by using WordNet or a thesaurus [2] makes good use of word relationships such as NT(narrow term), RT(related term), and so on.


As non-positional features, all words inside NEs are used.

these rules are non-deterministic (Figure 1), and we can change it to the deterministic FSt ( Figure 2) since the lengths of NEs are finite.


In Table 3, N E w0 denotes the rightmost word in an identified NE region.

the FSt is constructed with the GENIA 3.02 corpus.


Baseline2 is the two-phase ME model considering all words.

due to dynamic progress in biomedical literature, a vast amount of new information and research results have been published and many of them are available in the electronic form -for example, like the PubMed MedLine database.


Also, Table 6 shows the results of semantic classification.

if a word frequently occurs in other positions, we regard it has the property of a modifying noun.


While the performance in the standard domain turned out to be quite good as shown in the papers, that in the biomedical domain is not still satisfactory, which is mainly due to the following characteristics of biomedical terminologies: First, NEs have various naming conventions.

a lot of term occurrences in real text would not be identified with simple dictionary look-up, despite the availability of many terminological databases, as claimed in [12].


That kind of errors is caused mainly by the cascaded phenomenon in biomedical names.

in order to utilize other class terms, we additionally annotated "O" class words in the training data where they corresponds to other classes such as other organic compound, lipid, and multi cell in GENIA 3.02p version corpus.


4 Although this prior probability is quite accurate, it does not have sufficient applicability.

acronym can detect potential inclusion at 46.58 applicability and 96.05 accuracy.


In natural language processing community, bilingual dictionaries are useful in many areas, such as machine translation and cross language information retrieval.

if a word pair could be matched through two different languages, it is considered a very good match.


They also applied the semantic matching using part of speech and second-language matching.

eDICT and CeDICT make no difference on the parts of speech and therefore the translations in english can be in any form.


One can create a new word by combining the existing characters but it is hardly that one can create a new character.

for example, b (hair) and`(deliver) are simplified to .


A new bilingual dictionary can be built using two existing bilingual dictionaries, such as Japanese-English and English-Chinese to build Japanese-Chinese dictionary.

table 1 shows the results of using English as the pivot language and one time inverse consultation as the scoring function.


We extract only nouns, verbal nouns and verbs from this dictionary, and try to search for their translation equivalents in Chinese.

"discussion" is one of the translations in English as provided by EDICT.


Based on the assumption that two adjacent or overlapped temporal expressions refer to the same temporal concept, we combined them.

the data collection contains 285,746 characters/142,872 words and 4,290 manually annotated temporal expressions.


The above instances formed by non-anaphors would be labelled as "00".

the algorithm simply uses a unit of one as the increment and decrement.


The twin-candidate model itself could not identify and block such invalid resolution of non-anaphors.

-Create an instance by pairing M non ana , C rand , and each of the candidates other than C rand .


Features f 1-f 17 record the properties of a new markable and its two candidates, as well as their relationships.

the final record of a candidate is its won-lost difference in the round-robin matches.


Input: The result of morphological analysis:   Most previous research uses the speech act of previous utterance as context feature (CF1 in Use speech acts that pop in discourse stack and Sub-dialogue End (SE) else Use speech acts of previous utterance and Dialogue Continue (DC) End information for speech acts analysis [1].

this annotated dialogue corpus has 17 types of speech acts.


The shrinkage technique was verified in its efficiency for text classification tasks learned with insufficient training data.

this paper proposes to use a speech acts hierarchy and a discourse stack for improving the accuracy of classifiers in speech acts analysis.


nominal anaphora recognition is approached by filtering those anaphor candidates, which have no referent antecedents or which have antecedents but not in the target biomedical semantic types.